SISG MODULE 19

This module will provide students with the basic tools to carry out genetic association analysis within the context of genome wide association studies (GWAS) and next-generation sequencing studies with considerable emphasis on hands-on learning.  Familiarity with R is assumed, and PLINK advised.

Topics covered include: case-control (disease) association testing; quantitative trait analysis; quality control processes in GWAS; multi-locus testing using gene and pathway information; population structure and ancestry inference; association testing in the presence of population structure and/or relatedness; gene-environment and gene-gene interactions; basic rare variant association analysis in sequencing studies; advanced rare variant methods; sequence kernel association tests (SKAT); meta-analysis; design considerations; and other emerging topics.

An important component of this module is in-class software exercises which will provide students with hands-on experience analyzing real data using state-of-the-art analysis tools for GWAS and next generation sequencing data.

Learning Objectives: After attending this module, participants will be able to: 

1. Perform SNP-based association testing with adjustment for covariates in R, including estimation of principal components analysis (PCA) for population structure inference and correction in a GWAS.

2. Create Manhattan plots and quantile-quantile plots in R from PLINK GWAS results.

3. Perform multi-loci association testing in PLINK using gene and pathway information.

4. Perform a linear mixed model (LMM) for GWAS with relatedness and/or population structure. 

5. Test gene-gene and gene-environment interactions.

6. Run sequence kernel association tests and other advanced methods for rare variant association methods.

7. Perform genetic meta-analysis.

Loic Yengo is Associate Professor of Statistical Genetics in the Institute for Molecular Bioscience at the University of Queensland in Brisbane, Australia. His group develops and applies novel statistical methods to analyse large volumes of genomic data. He has also contributed to improving understanding of the genetic and phenotypic consequences of non-random mating (inbreeding and assortative mating) in human populations. Learn more about Loic’s work here.

Joelle Mbatchou is a statistical geneticist at the Regeneron Genetics Center where her research focuses on developing statistical methods and computational tools for large-scale genetic association analyses to better understand the impact of genetic variation on human disease.  She has developed tools, including REGENIE, for efficient modeling and applications to large-scale biobanks. . Access Joelle’s work here.